Hospital Patient Footfall Predictive Model

Heramb Vengurlekar Thadomal Shahani Engineering College vengurlekarheramb19@gmail.com
Code

Keywords: Machine Learning, scikit-learn, Regression, EDA, Healthcare Analytics

Overview

A data-driven machine learning system developed to forecast hospital admission volumes, enabling healthcare administrators to optimize staffing, bed occupancy, and medical inventory.

Methodology & Highlights

  1. Exploratory Data Analysis (EDA): Analyzed multi-year admission patterns, uncovering strong seasonal correlations, day-of-week variances, and anomalous footfall spikes.
  2. Feature Engineering: Built lag features, rolling averages, and calendar cyclical encodings.
  3. Model Training & Evaluation: Trained multivariate regression models using scikit-learn with k-fold cross-validation, optimizing for Mean Squared Error (MSE) and high R² accuracy.